Research ArticleStudy on Mobile Augmented Reality Adoption forMayo Language Learning

Erasmo Miranda Bojórquez,1 Osslan Osiris Vergara Villegas,2

Vianey Guadalupe Cruz Sánchez,1 Jorge Luis García-Alcaraz,2

and Jesús Favela Vara3

1Departamento de Ingenierıa Electrica y Computacion, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, CHIH, Mexico2Departamento de Ingenierıa Industrial y Manufactura, Universidad Autonoma de Ciudad Juarez, Ciudad Juarez, CHIH, Mexico3Departamento de Ciencias de la Computacion, Centro de Investigacion Cientıfica y de Educacion Superior de Ensenada,Ensenada, BC, Mexico

Correspondence should be addressed to Erasmo Miranda Bojorquez; al132803@alumnos.uacj.mx

Received 20 April 2016; Revised 10 August 2016; Accepted 28 August 2016

Academic Editor: Juan C. Cano

Copyright © 2016 Erasmo Miranda Bojorquez et al. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

This paper presents the results of a study applied to undergraduates in order to know how the cultural dimensions affect theirperceptions of the acceptance and use of new technologies in a student-centered learning environment. A total of 85 undergraduatestudents from the Autonomous Indigenous University of Mexico (UAIM) participated in the study. Each student was asked touse a mobile augmented reality (MAR) application designed to learn Mayo language (language spoken in Northwestern Mexico).Afterwards, the students responded to a survey with items concerning the use and technology acceptance and about culturaldimensions of individualism and uncertainty avoidance. Structural equationmodeling (SEM)was used to analyze the data collectedfrom students. Results provide evidence that the individualism contributes positively to perceived ease of use of the MAR app, anduncertainty avoidance has no impact. The findings showed that the MAR system could be easily used if it includes a natural way topromote collaborative work. In addition, to gain the trust of students, the uncertainty avoidance needs to be reduced by enrichingthe help information offered for app use.

1. Introduction

Progress in transportation technology has facilitated humanmobility. The mobility leads to increasing encounters amongpeople with different cultural backgrounds, frequentlygrouped in different cultural identities. Individuals musthave at least two main abilities known as interculturalcompetence and communication skills to succeed in theseencounters [1]. The former refers to the ability to efficientlyadapt to the new cultural contexts. The latter refers to theability to share emotions and create a relationship of effectiveempathy through the ability to use language.

Intercultural competence is invaluable in educationalfields. Schools offer opportunities to develop interculturalcompetence and communication skills. For example, at

university, the undergraduates have the necessity to shareinformation across different cultures and social groups.Therefore, the use of information and communication tech-nologies (ICT), such as augmented reality (AR), couldencourage great opportunities for cross-cultural collaborativework in almost all the topics covered in the learning environ-ments [2].

The effectiveness and efficiency of ICT deployment andits use are influenced by national and organizational cultures.During the last decades, culture has been one of the keyconstructs of research in a number of fields such as finance,learning, public relations, and decision making. In order tounderstand cultural differences, several models have beendeveloped, such as global leadership and organizationalbehavior effectiveness (GLOBE) [3], Hofstede et al. [4], and

Hindawi Publishing CorporationMobile Information SystemsVolume 2016, Article ID 1069581, 15 pageshttp://dx.doi.org/10.1155/2016/1069581

2 Mobile Information Systems

Trompenaars and Hampden Turner [5]. There are a numberof differences between these models including the timeframe of data collection, the type of individuals studied, thenumber of companies, and the globalization of information.However, Hofstede model was selected in this paper because(a) it is leading cross-cultural research in various disciplineswhich use quantitative methods; (b) it allows comparing andcontrasting the cultural characteristics of individuals againstthe cultural characteristics of their group, organization, andsociety; (c) it is flexibile; (d) it allows assessing a numberof social trends and phenomena in a systematic way, amongothers [6, 7].

Nowadays, one of the main forms of ICT is the mobiledevices. These devices can offer a great quantity of opportu-nities in the field of education, because they can be used asa learning tool offering students the possibility of capturingreal or simulated data and accessing a variety of informationanytime and anywhere. According to Khan et al. [8], thetype of learning which allows learners to obtain learningmaterials anywhere and anytime using mobile technologiesand the Internet is called mobile learning (m-learning).Mobile devices offer new opportunities to deploy applicationsaimed at developing intercultural competencies, because theyoffer portability and ubiquity. According to [9, 10], the threemain important aspects to use mobile devices in educationare (a) portability, (b) instant connectivity, and (c) contextsensitivity.

On the other hand, New Media Consortiums 2011 Hori-zon Report [11] has reported that AR is becoming a technicaltrend in higher education for making technology blend withvirtual and real worlds and is expected to reach mainstreamuse in education. Current definitions of AR assert that“AR allows a combination of real world elements capturedthrough a camera with multimedia elements such as text,images, video, or 3D models and animations” [12]. For thiswork, the definition proposed by Azuma [13], who was apioneer in this topic, was adopted. According to Azuma, ARis a system that fulfills three basic features: (1) a combinationof real and virtual worlds, (2) real time interaction, and (3)accurate 3D registration of virtual and real objects.

In the past, AR applications were created for execut-ing in personal computers. However, with the explosivedevelopment of powerful mobile devices, they have becomeconvenient platforms for deploying AR applications, sincethey include a camera for capturing the real world view,powerful processors to recognize and track the objects ofinterest, and the capability of rendering and displaying 3Dgraphics and video. This new area is known as mobileaugmented reality (MAR).

An opportunity to perform research that involves thetopics mentioned above was detected inside the learningenvironment of the Autonomous Indigenous University ofMexico (UAIM), located in Northwestern Mexico. Due to itsintercultural character, UAIM is focusing on supporting thepreservation of indigenous languages. UAIM’smain objectiveis to provide students, with different cultural backgrounds,with a set of student-centered learning tools that preservestheir cultural identity. This leads to stimulating studentscommunication skill in their mother language called Mayo

and also in Spanish andEnglish.The task of teaching-learningMayo is typically developed face to face among teachers andstudents, and great linguistic wealth is involved. However,a problem emerges when the teacher is absent, becausethe student cannot continue with the learning process. Theproblem is mainly because the support material for teachingis based on oral traditional media owned by the instructor.Therefore, it could be convenient to design a tool to supportstudents self-learning of Mayo language that can be usedeverywhere and anywhere, and this can be made by meansof a MAR system.

In this paper, a MAR system called Loterıa Mayo tosupport the Mayo language learning is presented. The mostimportant features that systemmust fulfill are that it needs tobe easy of use and take into account the cultural backgroundof the student. Therefore, the technology acceptance model(TAM) extended with the cultural factors of Hofstede modelwas used to evaluate the MAR system acceptance by 85students from UAIM.

The rest of the paper is organized as follows. In Section 2the concepts related to the impact of cultural issues onmobileapplications, the use of MAR for learning, and theories aboutTAM and cultural dimensions are provided. In Section 3 adescription of the MAR application is offered. In Section 4the evaluation of the MAR system is presented, includingthe objective, the hypotheses, the experimental design, theprocedure, the results, and the discussion. Finally, Section 5presents the conclusions of this research work.

2. Theoretical Background

In the following subsections, several important theoreticalconcepts related to the study presented are briefly explained.

2.1. The Impact of Cultural Issues on Mobile Applications.Mobile devices offer new opportunities to deploy applicationsaimed at developing intercultural competencies, because theyoffer portability and ubiquity features. Several authors havelinked the use of mobile devices with the elements related toculture and technology adoption.

The work proposed in [14] reports on gender patternsidentified in a cross-national study of mobile phone use byuniversity students in Sweden, USA, Italy, Japan, and Korea.The set of data was collected from multiple countries tocompare the role of gender versus culture (to the extentculture corresponds with nationhood) in use of and attitudestowards mobile telephony. Data were analyzed with respectto the purpose of communication, politeness issues, contactmanagement, and volume of use. Results indicated a numberof gendered usage and attitudinal patterns. However, in somecases, cultural variables may prove to be more explanatorythan gender.

The study presented in [15] examined the impact ofcultural differences on mobile phone adoption patterns. TheHofstedes cultural dimensions were used to examine culturaldifferences of USA and South Korea, and the Bass diffusionmodel was used to delineate innovation and imitation effectson mobile phone adoption. The results show that in USAculture innovation factor has a significantly higher level of

Mobile Information Systems 3

effect on adoption than it does in South Korea culture; andin South Korea culture imitation factor has a higher degreeof effect on adoption than it does in USA culture. Thesefindings imply that, in individualistic cultures, people tendto seek information on their own from direct and formalsources, whereas, in collectivistic cultures, people rely moreon subjective evaluation of an innovation, conveyed fromother like-minded individuals who already have adopted theinnovation.

The study presented in [16] examined the current stateof the students attitudes towards mobile technology use forsecond and foreign language learning in higher education.Moreover, the study investigated if age, gender, or culturalfactors affect these attitudes. A total of 345 students from twodifferent countries, China (Yunnan University) and Sweden(Dalarna University), participated in the study. To accesslearners perceptions towards mobile technology use, theKearneys pedagogical framework to mobile learning froma sociocultural perspective was used. Hofstedes culturaldimensions were used to approach students cultural views.The findings show the respondents attitudes towards mobilelearning are very positive with individualization being mostpositive (83%) followed by collaboration (74%) and authen-ticity (73%).

The work in [17] focused on the value consumers canpotentially gain from using mobile data services (MDS).The study contributes to the existing body of knowledgeabout the factors affecting the adoption of MDS, especiallyin Jordan. A value-based model was developed to investi-gate the effect of technological, social, and informationalinfluences on mobile value dimensions and the effect of thelatter on the adoption intention of MDS. Utilitarian andeconomic value dimensions are the best predictors of MDSadoption intention in Jordan, and mobile technology formsthe strongest influence on people’s perception of utilitarianand economic value dimensions. Social and informationalinfluences were also found to be significant but at a lesserdegree than technological influences.

In developing countries, especially in Africa, mobilebanking can play a strong role, providing a way to overcomefinancial exclusion and physical distance by allowing localpopulation to conduct financial transactions. An innovativeand comprehensive theoretical model that combines theextended unified theory of acceptance and use of technology,with cultural moderators from Hofstede, providing newinsights into factors affecting the acceptation and how cultureinfluences individual use behavior was presented in [18].The model was tested using structural equation modeling(SEM). Performance expectancy, hedonic motivation, andhabit were found to be the most significant antecedentsof behavior intention. To explain the mobile banking usebehavior, the habit and culture moderator effects on behaviorintention over use behavior were the most important drivers.Collectivism, uncertainty avoidance, short term, and powerdistance were found to be the most significant culturalmoderators.

The qualitative study in [7] explores the factors responsi-ble for creating economic barriers for 245 women in India,which prevent them from owning a mobile phone. Study

findings reveal the specific ways in which cultural factors,like (1) the long power distance between men and women,(2) the gender role defined by Indian society for women,(3) womens attitudes of avoiding uncertainty, and (4) collec-tivistic practices, create economic barriers for the financiallyindependent study participants. Due to the unfair economicdisadvantages generated by the above cultural factors, itbecomes challenging for the participants earning a little lessthan 2 dollars a day to own some of the least expensivemobilephone handsets worth 15 or so on installments of one dollara month.

Asmentioned before, several studies about cultural issueson mobile devices and applications have been presented inthe literature; however, the cultural aspect is just beginningto be researched in particular with respect to the acceptanceof using mobile technology in learning. Mobile technologiescan provide newopportunities to learn a foreign language andinclude benefits such as flexibility, small size, low cost, andfriendly use.

2.2. Mobile Augmented Reality and Its Use for Learning. Forthe particular case of use of MAR in learning environments,the researchers such as those in [19–21] have suggestedthat the student can strengthen their learning based on themixture of the real and the virtual practices. According tothe study in [20], MAR can enhance the traditional learningmodels. The work in [22] explains that the potential powerof MAR as a learning tool is its ability to “enable studentsto see the world around them in new ways and engagewith realistic issues in a context with which the students arealready connected.” In addition, the work presented in [23]shows that MAR is a promising tool for learning becauseof its ubiquitous availability and computing power offeredby mobile devices. However, a challenge remains due tointegration problemswith traditional learningmethods, costsin the development and maintenance of MAR systems, andresistance to the use and acceptance of new technologies.

MAR systems are classified into two types: (a) location-aware and (b) vision-based.The former presents digitalmediato learners as they move through a physical area with a GPS-enabled mobile device. The latter presents the digital media(i.e., text, graphics, audio, video, and 3D models) to learnerswhen they point the camera of a mobile device to certainobjects (e.g., QR codes and images) [24].

By all of the above, MAR can be a new way of learningwhich uses the unique capabilities of mobile devices (flexibil-ity, mobility, and efficiency) in order to offer to the studentsthe opportunity to continue the learning process inside andoutside the classroom [25].

2.3. Technology Acceptance Model (TAM). In 1989, Davisproposed the TAM which is an information systems theorythat models how users come to accept and use a technology[26]. The TAM suggests that the use of a system is a responsethat can be explained or predicted by the motivation of theuser and is directly influenced by an external stimulus of thesystem features and capabilities [27].

The TAM indicates that perceived ease of use (PEOU)and perceived usefulness (PU) are the two main beliefs that

4 Mobile Information Systems

determine people intention to use (ITU) technology. PEOUrefers to the degree to which a person believes that usinga particular system would be free from effort, while PUrefers to the degree to which the user believes that a systemwould improve his or her work performance [28–30]. In theTAM, the degree of technology acceptance ismeasured by theITU.

In the literature, there have been several studies about theacceptance and use of technologies particularly focused onmobile devices [16, 25, 27, 31–35]. In most of the works, theTAMwas used by adding particular aspects about limitationsof the mobile applications and cultural dimensions [33, 36].In this paper, the TAM has been used because it is consideredas appropriate to predict student satisfaction in learningcontexts.

2.4. Culture and Cultural Dimensions. Culture was definedin [6] as the collective programming of the mind whichdistinguishes the members of one human group fromanother. Cultural factors are the established beliefs, values,traditions, laws, and languages of a nation or society. Thefactors are durable and transcending for the developmentof individuals and they suggest that all these factors can befound behind the different users while using and adoptinga particular system [16]. Therefore, it could be of greatinterest to study learning environments where a mixtureof students from diverse cultural backgrounds is included[37].

The study by Hofstede is important because it revealshow people of different countries and cultures interactaccording to their cultural dimensions. Hofstede provides avalues survey measure (VSM 2013) that includes six culturaldimensions tomeasure and compare different cultures, whichare (1) power distance (PD), (2) uncertainty avoidance (UA),(3) individualism versus collectivism (IDV), (4) masculinityversus feminity (MASC), (5) long term orientation versusshort-term normative orientation (LTO), and (6) indulgenceversus restraint (IVSR) [38].

PD is “the extent to which a society accepts the factthat power in institutions and organizations is distributedunequally.” UA is “the extent to which a society feels threat-ened by uncertain and ambiguous situations and tries toavoid these situations by providing greater career stability,establishing more formal rules, not tolerating deviant ideasand behaviors, and believing in absolute truths and the attain-ment of expertise.” IDV is “the degree to which people in acountry prefer to act as individuals rather than asmembers ofgroups.” MASC is “the extent to which the dominant valuesin society are masculine that is, assertiveness, the acquisitionof money and things.” LTO refers to “future oriented valueslike persistence and thrift, while a short-term orientationrefers to past and present oriented values like respect fortradition and fulfilling social obligations.” Finally, IVSR is“the degree to which culture facilitates to regulate the desiresand motivation of basic needs.”

All the theoretical concepts described provide the basisfor the development of the MAR system for learning Mayolanguage which will expand in the following sections.

Figure 1: Several cards of the deck used as markers.

3. Lotería Mayo Application

The application created for this study was called LoterıaMayo. It is based on images and audio using MAR througha card game. The application was inspired by the traditionalMexican game called Loterıa (lottery), which is similar tobingo but uses images on a deck of cards instead of numberedping pong balls. The deck is composed of a set of 89 differentimages; the drawings in each card represent typical objects,animals, and people from the Mayo social group and wereused as markers for the MAR system. All the drawings wereprovided by professors, in PDF format, who teach the Mayolanguage at UAIM. An example of several card images isshown in Figure 1.

Before the start of each game, players choose what table(tabla) they want to play with. The table (shown in Figure 2)is a board with a randomly created 4 × 4 grid of pictures withtheir corresponding name. The game starts with the cantorrandomly selecting a card and showing it to the camera ofthe mobile device; immediately, the process of recognizingan object starts by comparing the extracted features againstthe database of 89 objects previously defined as image targets.If a known object is recognized, then the real model issuperimposed in the real scene, the corresponding audiowiththe pronunciation of the Mayo word is reproduced followedby an explanation in Spanish language, and the process oftracking is initiated. This process is repeated every time thecamera points at a card which means that the student canrepeat this process to learn. If the image of the card appearson the player table, the playermarks the corresponding imagewith small rocks or beans. The process is repeated until thefirst player completes all the images in the table and shoutsLoterıa! An example of augmentation results can be observedin Figure 3.

The system was developed with Unity and Vuforia plat-forms, and the camera of the mobile device was used torecognize the card drawings. The MAR system was initiallyinstalled for tests, on a laptop withWindows 7 operating sys-tem. Subsequently, the system was installed on a Sony XperiaZ1, on Asus and Samsung Galaxy tablets. The application can

Mobile Information Systems 5

Figure 2: An example of one board (table) for lottery game.

be executed on devices with Android operating system 4.2.1and up.

Unity, that is, a multiplatform game engine created byUnity Technologies, was selected because of the facility ofhaving control of the content of the mobile device in alocal way. The visual environment of the platform providesintegration with Vuforia that allows the creation of AR con-tent, including the most important operations such as objectrecognition and tracking. The drawings of each card weredefined in Vuforia as image targets and renamed using theirrespective word in Mayo language for easy identification.Then, a test to observe the capability of recognition for eachobject was executed, obtaining a great success. At the end,a digital image texture representing the real object for eachdrawing and its corresponding audio in mp3 format wasassigned to each image target plane. An example of the realmodels superimposed on the card drawings is shown inFigure 4.

4. Evaluation of Lotería Mayo

In order to explore the potentials and limitations of thesystem, a study was performed to assess the PEOU and PUthat influence the ITUMAR system.

4.1. Objective of the Evaluation. The aim of the study was toassess the MAR system through variables related to the per-ception and the cultural dimensions of the undergraduates to

predict the ITUMAR system. The study is relevant becauseit will allow the process of system redesign including thecharacteristics of PEOU, PU, and culture of the students.Thiscould increase the probability that the MAR system will beused inside the learning environments.

4.2. Research Hypothesis Development. The TAM was orig-inally designed to provide an explanation of user behaviorwhen using information systems [28]. An objective of TAMis to establish the basis for tracing the impact of externalfactors, internal beliefs, attitudes, and intentions of users.TheITUMAR system is determined by PEOU and PU of the user.Therefore, the TAM can be used to identify the reasons whya system is unacceptable by the user and as a consequencedesign the appropriate corrective steps.

In this study, the TAM was used to observe the percep-tions of undergraduates in the task of learning the Mayolanguage through a MAR system. The proposed model isan extension of TAM due to the addition of two culturaldimensions extracted from Hofstede model [38].

According to TAM, the PU and the PEOU affect thestudent intention to adopt a new technology. In fact, the lesseffort the students expect to invest in using the technology,the more useful they perceive it to be [39]. Therefore, thefollowing three hypothesis can be postulated:

(𝐻1) Students PU of MAR will positively affect theirITUMAR system.

(𝐻2) Students PEOU of MAR will positively affecttheir ITUMAR system.

(𝐻3) Students PEOU ofMARwill positively affect PUof the MAR system.

According to Hofstede et al. [4], culture is specific tothe group or society and there is no absolute measure forevaluating it. Therefore, culture is described by externalobservers based on the manifestations of common practicesand values.The common practices are composed of symbols,heroes, and rituals, and the values are considered as the coreof the culture that is acquired and developed at an earlyage. Hofstede et al. refer to the set of culture values as thenational value system. Hofstede’s cultural dimensions are thetools used to measure the acceptance and use of MAR systemaccording to cultural diversity of undergraduates [37]. In thisstudy, the cultural dimensions of IDV and UA were selected,because of the conditions of the learning environment at theUAIM. In the daily environment of UAIM undergraduates,the students work individually or collectively faced withuncertainty of the new technologies. Therefore, according to[6, 15, 16, 40], two hypotheses associated with cultural factorscan be postulated:

(𝐻4) Students IDVwill positively affect their PEOUofthe MAR system.

(𝐻5) Students UA will positively affect their PEOU ofthe MAR system.

4.3. Experimental Design and Procedure. Once the hypothe-ses have been established, the proposed extended TAM can

6 Mobile Information Systems

(a) (b)

Figure 3: Example of augmentation obtained with Loterıa Mayo: (a) with a mobile device and (b) with a laptop.

Figure 4: The real models superimposed on the plane of card drawings.

be defined as shown in Figure 5. The opportunity of studentsto continue the learning process inside and outside theclassroom can be improved and become more efficient byincluding the characteristics obtained from the results of thesurvey applied related to TAM, which predicts the ease ofuse offered by the MAR system. Another factor that alsoinfluences the use of the MAR system is the set of behaviorsand languages learned by the student within his social group(cultural dimensions). The justification for relating IDV andUA with the latent variable PEOU has two assumptions: (1)the ability to take advantage of what students have learned towork collectively inside their social group and (2) the properhandling of uncertainty to new technologies, facilitating theuse of MAR system through strategies such as collaborativeor group work. Both assumptions can happen in the Loterıagame.

The study was performed at classrooms of UAIM, locatedin Los Mochis, Sinaloa, Mexico. The sample consisted of atotal of 85 indigenous undergraduates from different levelsand academic programs, and a within-subjects design wasused.

The session to perform the study consisted of an expla-nation offered to undergraduates about the goal of theresearch and the corresponding instructions to manipulatethe MAR application. Afterwards, the students randomlyselected ten cards from the deck of 89 cards and started theapplication use.Three tablets were used; thus 3 students couldsimultaneously test the application. After that, another threestudents would do the same; the process was repeated until allthe students had a chance to test the application. The averagetime spent by each student was 5 minutes. Finally, with thegoal of examining the hypothesis, a printed survey was filled

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Intention to usemobile AR(ITUMAR)

Perceivedease of use

(PEOU)

Perceivedusefulness

(PU)

Uncertaintyavoidance

(UA)

Individualism(IDV)

H1

H2

H3

H4

H5

Figure 5: The proposed research model.

in by each student in order to know their perception of the useof theMAR system.Data collection took place during the firstweek of July 2015.

The survey (shown in the Appendix) was designed andstructured in three main sections. The first one is composedof three questions to collect student personal data such as age,gender, and ethnicity. The second one includes 9 statementsregarding the PEOU, PU, and ITUMAR for learning theMayo language, adapted from English version of “VSM 2013questionnaire” [38]. Finally, the third one includes 8 itemsrelated to TAM cultural dimensions of IDV and UA.

The constructs were valued based on 4 different seven-point Likert scales: Scale A corresponds to the following: 1Strongly disagree, 2 In disagreement, 3 Somewhat disagree, 4I do not know, 5 Somewhat agree, 6 In agreement, 7 Stronglyagree. Scale B corresponds to the following: 1 Strongly unim-portant, 2 Without unimportant, 3 Somewhat unimportant,4 I do not know, 5 Somewhat important, 6 Important, 7Very important. Scale C corresponds to the following: 7 Verygood, 6 Good, 5 Little good, 4 Fair, 3 Little poor, 2 Poor,1 Very poor. And Scale D corresponds to the following: 1Usually, 2 Sometimes, 3 Little, 4 I do not know, 5 It is rare, 6Seldom, 7Never.The latent variableswith their correspondingindicators and scales are depicted in Table 1.

The data analysis of this study was based on a SEMapproach, because it can help to represent the relationshipsamong the latent variables. The SEM has been proven toallow the simultaneous analysis for assessing the relationshipsamong variables, and the errors for each variable can beindependently estimated [41]. The structural model (pathmodel) that visually depicts the hypotheses and the latentvariables with their corresponding indicators is shown inFigure 6. The indicators locked into rectangles correspond tothe statements of the survey.

The SmartPLS 3 software, that is, a tool for partialleast squares structural equation modeling (PLS-SEM), wasused to examine the relationships between indicators and to

Table 1: Latent variables indicators with their corresponding Likertscales.

Indicators PEOU PU ITUMAR IDV UAPU1 Scale APU2 Scale APU3 Scale APU4 Scale APEOU1 Scale APEOU2 Scale APEOU3 Scale AITUMAR1 Scale AITUMAR2 Scale ACF1 Scale BCF2 Scale BCF3 Scale BCF4 Scale BCF5 Scale CCF6 Scale DCF7 Scale ACF8 Scale A

confirm if the proposed hypotheses were valid. Before analyz-ing and discussing the SEM, the reliability and validity of themeasurement model were checked through the indicators.It is necessary to determine whether the measures of thedata have satisfactory properties such as internal consistencyreliability, convergent validity, and discriminant validity.

4.3.1. Internal Consistency Reliability. The goal is to obtaina measure based on the correlations among different itemsof the same instrument. It is important to measure whetherseveral items proposed tomeasure the same general constructproduce similar scores. Cronbach’s alpha (𝛼) quantifies theinternal consistency of the data through the degree to which

8 Mobile Information Systems

Intention to usemobile AR(ITUMAR)

Perceivedease of use

(PEOU)

Perceivedusefulness

(PU)

Uncertaintyavoidance

(UA)

Individualism(IDV)

H1

H2

H3

H4

H5

CF1

CF2

CF4

CF3

PEOU2

PEOU3

PEOU1

ITUMAR1

ITUMAR2

CF5

CF6

CF8

CF7

PU1

PU2

PU4

PU3

Figure 6: Structural model with paths, latent variables, and indicators.

the items on an instrument are correlated. 𝛼 normally rangesbetween 0 and 1, where values from 0.0 to 0.40 are consideredlow, from 0.40 to 0.60 are considered moderate, from 0.60 to0.80 are considered good, and from 0.80 to 1.0 are consideredhigh.

4.3.2. Convergent Validity. The convergent validity can beestablished by showing that measures that should be relatedare in reality related. Convergent validity is the degree towhich a measure correlates positively with the alternativemeasures of the same construct.The items that are indicators(measures) of a specific construct should converge or sharea high proportion of variance. Therefore, to measure theconvergent validity, the average variance extracted (AVE)wasused.

The AVE is the average amount of variation that a latentconstruct is able to explain in the observed variables to whichit is theoretically related. It is considered adequate when itsvalue is ≥0.5; this indicates that, on average, a constructexplains more than half of the variance of its indicators.

4.3.3. Discriminant Validity. Discriminant validity is theextent to which a construct is truly distinct from otherconstructs by empirical standards. This implies that a con-struct is unique and captures phenomena not represented byother constructs in the model. Twomeasures of discriminantvalidity were used in this study, the Fornell-Larcker criterionand the cross loadings [42].

The Fornell-Larcker criterion suggests that discriminantvalidity is established if a latent variable accounts for morevariance in its associated indicator variables than it shareswith other constructs in the same model. To satisfy thisrequirement, the square root of the AVE of each constructshould be greater than its highest correlation with any otherconstruct. On the other hand, the cross (outer) loading of

an indicator on the associated construct should be greaterthan all of its loadings on other constructs.

4.4. Results. From the total sample of 85 students, 49% (42)were male and 51% (43) were female. The predominant ageof the students comprises the following: 45% (38) rangedbetween 20 and 24 years, 24% (20) between 25 and 29 years,and 11% (9) between 30 and 34 years. Regarding the questionof membership to an ethnic group, 12% (10) of studentsresponded with yes and 88% (75) said no.The correspondingvalues of 𝛼, mean, median, and standard deviation for thelatent variables of the proposed SEM are shown in Table 2.

As can be observed in Table 2, 𝛼 values for this researchranged from 0.701 to 0.921; therefore, the reliability of datais valid. Furthermore, the internal consistency of the latentvariables oscillates between good and high values, whichwereranged from 0.60 to 1.0.The computed values of AVE for eachlatent variable are shown in Table 3.

Table 3 shows that all the AVE values are higher than 0.5,which defines a positive correlation and as a consequenceconvergent validity.The Fornell-Larcker values computed forthe proposed model are shown in Table 4, and the crossloadings values are shown in Table 5.

As Table 4 demonstrates, the AVE square root for eachconstruct is greater than its shared variance with any otherconstruct. Furthermore, as can be observed from Table 5, theouter loadings of a construct are higher than all its cross load-ings with other constructs; therefore discriminant validity issupported. Once confirming that the data in the indicatorsand variables were reliable and valid, the next step was toevaluate the results of the SEM. This involves examining themodel predictive capabilities and the relationships betweenthe constructs. For this, the significant level of the pathcoefficients, the coefficient of determination (𝑅2), and thetotal effects of each latent variable were computed.

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Table 2: Descriptive statistics of indicators.

Latent variables Mean Median Std. dev. 𝛼

TAM variablesPerceived usefulness (PU) 0.921PU1 6.14 7 1.21PU2 6.19 6 0.98PU3 6.07 6 1.14PU4 6.16 7 1.24

Perceived ease of use (PEOU) 0.908PEOU1 5.89 6 1.21PEOU2 6.21 6 1.18PEOU3 6.14 7 1.38

Intention to use the MAR (ITUMAR) 0.701ITUMAR1 6.13 6 1.33ITUMAR2 6.18 6 1.12

Cultural factors (CF)Individualism (IDV) 0.919CF1 6.65 7 0.92CF2 6.69 7 1.02CF3 6.69 7 1.08CF4 6.58 7 1.29

Uncertainty avoidance (UA) 0.840CF5 6.38 6 1.48CF6 4.12 5 2.14CF7 4.22 5 2.54CF8 5.25 6 2.58

Table 3: AVE values for latent variables of the proposed model.

Latent variables AVEITUMAR 0.765IDV 0.799PU 0.805PEOU 0.846UA 0.590

Table 4: The Fornell-Larcker values of the latent variables of theproposed model.

Latent variables IDV ITUMAR PU PEOU UAIDV 0.894ITUMAR 0.655 0.875PU 0.212 0.599 0.897PEOU 0.452 0.769 0.742 0.920UA 0.776 0.530 0.037 0.276 0.768

The estimation of path coefficients in SEM is based onordinary least squares (OLS) regressions of each endogenouslatent variable on its corresponding predecessor constructs.The path coefficients may be biased if the estimation involvessignificant levels of collinearity among the predictor con-structs [41]. Therefore, it is important to measure the level ofcollinearity inside the model. Two measures were computed

Table 5: Results from the cross loadings computation for theproposed model.

Indicators Latent variablesIDV ITUMAR PU PEOU UA

CF1 0.92 0.60 0.27 0.51 0.67CF2 0.95 0.60 0.21 0.37 0.71CF3 0.91 0.65 0.19 0.42 0.75CF4 0.79 0.41 −0.10 0.16 0.72CF5 0.76 0.49 0.00 0.26 0.92CF6 0.64 0.48 0.08 0.25 0.91CF7 0.56 0.22 −0.14 −0.02 0.63CF8 0.54 0.19 −0.26 0.02 0.54ITUMAR1 0.53 0.92 0.64 0.77 0.39ITUMAR2 0.64 0.83 0.38 0.55 0.58PEOU1 0.35 0.65 0.70 0.87 0.25PEOU2 0.41 0.69 0.67 0.93 0.21PEOU3 0.49 0.77 0.68 0.95 0.30PU1 0.10 0.44 0.91 0.57 −0.10PU2 0.06 0.38 0.87 0.55 −0.02PU3 0.27 0.56 0.92 0.67 0.07PU4 0.27 0.69 0.89 0.80 0.13

to do this: the tolerance (TOL) and the variance inflationfactor (VIF).

10 Mobile Information Systems

Table 6: Results obtained for TOL and VIF.

Latent variables Indicators TOL VIF

PU

PU1 0.162 4.014PU2 0.249 3.175PU3 0.222 3.713PU4 0.173 2.395

PEOUPEOU1 0.262 2.201PEOU2 0.156 4.639PEOU3 0.126 5.356

ITUMAR ITUMAR1 0.251 1.411ITUMAR2 0.376 1.411

IDV

CF1 0.190 3.356CF2 0.158 5.179CF3 0.174 3.555CF4 0.283 2.224

UA

CF5 0.283 1.980CF6 0.345 2.123CF7 0.397 2.112CF8 0.440 1.696

The TOL represents the amount of variance of oneformative indicator not explained by the other indicators inthe same block. The VIF is a measure of the degree of multi-collinearity among the latent variables that are hypothesizedto affect another latent variable. It measures how much thevariance of an estimated regression coefficient is increased(inflated) because of collinearity. A TOL value of 0.20 orlower and a VIF value of 5 and higher, respectively, indicate apotentially harmful multicollinearity problem. The softwarestatistical package for the social sciences (SPSS) was used tocompute the TOL and VIF values of the proposed model; theresults obtained are shown in Table 6.

According to VIF values in Table 6, there are no criticallevels of collinearity, except for two indicators: PEOU3 andCF2. Moreover, the TOL values for 7 of the 17 variables arenot ≥0.2; however, 15 variables are <5 for VIF. Therefore, themulticollinearity problem does not need to be solved.

ThePLS-SEMalgorithmapplies partial regressionmodelsto estimate and represent the hypothesized relationshipsbetween the latent variables [41]. The estimated path coef-ficients values range between −1 and +1. Coefficients closeto +1 represent strong positive relationships, whereas strongnegative relationships are definedwhen the values are close to−1. The 𝑅2 coefficient is a measure of the predictive accuracyof themodel; it took into account the weight of each indicatorto identify which specific element should be addressed tosuggest positive action. The corresponding values betweenrelationships and 𝑅2 are shown in Figure 7.

The significance level of the path coefficients can be foundin two ways: the first one consists in comparing a criticalvalue against a value 𝑡 of theoretical student; the second oneconsists in verifying whether within the limits of the intervalsof confidence the zero value is included. The significancelevel of a path depends on the standard error coefficientfor computing the critical value of 𝑡 compared with the

theoretical value of 𝑡 = 1.96 with an error of 5% for a two-tailed 𝑡-test.

In addition to computation of 𝑡 values, several researchersreport the 𝑝 value to measure the significant level of apath. PLS-SEM assumes [41] that the data are not normallydistributed; this involves using a nonparametric bootstrapprocedure to test if the outer weights, the outer loadings,and the path coefficients are significant in the analysis.The bootstrapping routine uses 500 samples with randomcoefficient values to achieve a distribution that can be seenas a reasonable approximation of the distribution estimatedcoefficients, and the standard deviation can be used tocalculate the standard error through software. The computedvalues for 𝑡, standard error, confidence intervals, 𝑝 of latentvariables, and significance level are shown in Table 7.

FromTable 7, the limits of the confidence interval variablelatent paths for IDV andPEOUwere calculated as follows: thelower limit: 0.599− 1.96∗ 0.260 = 0.089, and the upper limit:0.599 + 1.96 ∗ 0.260 = 1.108. The value of 0.599 correspondsto the original value of the coefficient. 1.96 is the critical valueof 𝑡 for an error of 5%, and the value of 0.260 is the standarderror. The values of the outer weights for each indicator areshown in Table 8, whereas the indirect and total effects oflatent variables are shown in Tables 9 and 10, respectively.

Finally, according to Hofstede VSM 2013 manual, thecultural factors were also calculated. The IDV index wasobtained with

IDV = 35 (CF2 − CF1) + 35 (CF4 − CF3) + 𝐶 (𝑖𝑐) . (1)

The UA index is obtained with

UAI = 40 (CF5 − CF6) + 25 (CF7 − CF8) + 𝐶 (𝑢𝑎) . (2)

To calculate (1) and (2), the averages of each indicatormust be computed. 𝐶(𝑖𝑐) and 𝐶(𝑢𝑎) are positive or negativeconstants used to ensure that the index is inside the rangeof 0–100. The indices obtained for students of the UAIM areshown in Table 11.

4.5. Discussion. The sample size of the study was 85 students,and the latent variables used were UA, IDV, PEOU, PU, andITUMAR. After the analysis of the data collected, seventeenindicators were obtained: 8 related to cultural factors (4 forUA and 4 for IDV), 4 related to PU, 3 related to PEOU, and 2related to ITUMAR.

The characteristics of the data such as sample size,nonnormal, and the use of differentmeasurement scales, werethe main reasons to use PLS-SEM in this study. PLS-SEMworks efficientlywith small samples size and complexmodels,because it reaches high levels of statistical power with thesamples available [41, 43, 44]. The work in [45] presents thecriteria to take into account to establish the minimum sizeof the sample to have valid data by means of PLS-SEM. Asynthesized version of the criteria established by Cohen, ina form of table, can be found in [41], and it was used inthis study. According to the five independent variables, aminimum value to detect 𝑅2 of 0.25 and an estimation of 5%error probability were selected. As a result, Hair et al. suggest

Mobile Information Systems 11

(ITUMAR)

Uncertaintyavoidance

(UA)

Individualism(IDV)

(PU)

(PEOU)

0.599

0.063

0.723

0.742

−0.189

R2= 0.219

R2= 0.550

R2= 0.594

Figure 7: 𝑅2 and path coefficients of the proposed SEM.

Table 7: Path coefficients of the SEM proposed.

Latent variables paths Path coefficients 𝑡 Standard error Significance level 𝑝 Confidence intervalsIDV→ PEOU 0.599 2.304 0.260 ∗ ∗ ∗ 0.022 [0.089, 1.108]PU→ ITUMAR 0.063 0.289 0.219 NS 0.773 [−0.366, 0.493]PEOU→ ITUMAR 0.723 3.752 0.193 ∗ ∗ ∗ 0.000 [0.345, 1.100]PEOU→PU 0.742 9.096 0.082 ∗ ∗ ∗ 0.000 [0.582, 0.902]UA→ PEOU −0.189 0.873 0.216 NS 0.383 [−0.613, 0.235]∗∗∗Significance level of 5%.NS: no significance.

Table 8: Outer weights significance testing results.

Latent variables Indicators Outer weights 𝑡 Standard error Significance level 𝑝 Confidence intervals

IDV

CF1 0.384 (0.923) 2.618 0.147 ∗ ∗ ∗ 0.009 [0.096, 0.671]CF2 0.281 (0.945) 3.909 0.072 ∗ ∗ ∗ 0.000 [0.140, 0.422]CF3 0.315 (0.908) 3.360 0.094 ∗ ∗ ∗ 0.001 [0.131, 0.498]CF4 0.120 (0.791) 0.705 0.170 NS 0.481 [−0.214, 0.454]

UA

CF5 0.555 (0.916) 1.616 0.344 NS 0.107 [−0.118, 1.229]CF6 0.536 (0.911) 1.518 0.353 NS 0.130 [−0.156, 1.227]CF7 −0.035 (0.633) 0.105 0.332 NS 0.917 [−0.686, 0.616]CF8 0.048 (0.538) 0.202 0.239 NS 0.840 [−0.421, 0.517]

ITUMAR ITUMAR1 0.667 (0.919) 7.994 0.083 ∗ ∗ ∗ 0.000 [0.503, 0.830]ITUMAR2 0.468 (0.828) 8.104 0.058 ∗ ∗ ∗ 0.000 [0.355, 0.581]

PEOUPEOU1 0.343 (0.871) 18.523 0.019 ∗ ∗ ∗ 0.000 [0.307, 0.380]PEOU2 0.357 (0.933) 26.714 0.013 ∗ ∗ ∗ 0.000 [0.331, 0.383]PEOU3 0.385 (0.954) 19.149 0.020 ∗ ∗ ∗ 0.000 [0.346, 0.425]

PU

PU1 0.242 (0.911) 5.160 0.047 ∗ ∗ ∗ 0.000 [0.150, 0.334]PU2 0.224 (0.869) 5.499 0.041 ∗ ∗ ∗ 0.000 [0.144, 0.304]PU3 0.293 (0.922) 9.345 0.031 ∗ ∗ ∗ 0.000 [0.232, 0.355]PU4 0.355 (0.886) 4.589 0.077 ∗ ∗ ∗ 0.000 [0.203, 0.507]

∗∗∗Significance level of 5%.NS: no significance.

12 Mobile Information Systems

Table 9: Indirect effects of latent variables.

Latent variables Indirect effects 𝑡 Significance levels 𝑝

IDV→ ITUMAR 0.461 2.199 ∗ ∗ ∗ 0.028IDV→ PU 0.444 2.422 ∗ ∗ ∗ 0.016IDV→ PEOU — — — —PU→ ITUMAR — — — —PEOU→ ITUMAR 0.047 0.278 NS 0.781PEOU→ PU — — — —UA→ ITUMAR −0.145 0.851 NS 0.395UA→ PU −0.140 0.890 NS 0.374UA→ PEOU — — — —∗∗∗Significance level of 5%NS: no significance.

Table 10: Total effects of latent variables.

Latent variables Total effects 𝑡 Significance levels 𝑝 95% confidence intervalsIDV→ ITUMAR 0.461 2.199 ∗ ∗ ∗ 0.028 [0.050, 0.871]IDV→ PU 0.444 2.422 ∗ ∗ ∗ 0.016 [0.085, 0.804]IDV→ PEOU 0.599 2.304 ∗ ∗ ∗ 0.022 [0.089, 1.108]PU→ ITUMAR 0.063 0.289 NS 0.773 [−0.366, 0.493]PEOU→ ITUMAR 0.769 14.636 ∗ ∗ ∗ 0.000 [0.666, 0.873]PEOU→ PU 0.742 9.096 ∗ ∗ ∗ 0.000 [0.582, 0.902]UA→ ITUMAR −0.145 0.851 NS 0.395 [−0.480, 0.190]UA→ PU −0.140 0.890 NS 0.374 [−0.449, 0.168]UA→ PEOU −0.189 0.873 NS 0.383 [−0.613, 0.235]∗∗∗Significance level of 5%NS: no significance.

Table 11: Cultural dimensions index.

Member of an ethnic group Cultural factorsIDV UA

Yes 44.50 87.50No 12.91 63.45Total 16.67 66.31

a minimum of 70 observations for the sample size. Also,the minimum sample size was calculated with the approachcalled the rule of 10 times. The rule establishes that theminimum number of samples required for valid data can beobtained by multiplying by 10 times the maximum numberof formative indicators of a latent variable. In this study,the latent variable with the highest number of indicatorswas PEOU with 8 indicators coming from the IDV andUA variables. Therefore, 8 multiplied by 10 results in theminimum required sample size of 80. Based on the resultsobtained with the table of Hair (70 observations) and the ruleof 10 times (80 observations), the asseveration that the datacollected from 85 observations for this study was sufficient issupported.

A summary of the valid criteria used to perform the studyis shown in Table 12.

It should be noted from Table 12 that the model proposedaccomplishes all the accepted criteria; therefore, the interpre-tation of the results can be performed.

Table 12: Summary of accepted criteria for checking reliability andassessment of the proposed model.

Reliability and assessment factorsof model Accepted criterion

𝛼 Values between 0.6 and 1.0AVE >0.5

Cross loadings of indicators withtheir latent variables

Higher values of theindicators with their latent

variablesLevels of collinearity TOL ≥ 0.20 and VIF < 5Outer weights Search for higher valuesPath coefficient relationships Values between −1 and +1

Path coefficient significanceCritical values > 𝑡

distribution = 1.96 (error5%)

No significance in confidenceintervals

0 is inside the lower andupper limits of confidence

intervals

𝑅2Values: 0.25 weak, 0.50medium, 0.75 substantial

IDV index Values between 0 and 100UA index Values between 0 and 100

As can be observed in Figure 7, the path coefficientrelationship of IDV contributes to PEOU with a positive

Mobile Information Systems 13

value of 0.599, and UA contributes with a value of −0.189which is not statistically significant. According to Table 6,the indicators CF2 and PEOU3 have VIF values greater than5, which means that those indicators have critical levelsof collinearity. However, the indicators were not removedfrom the survey because, in Table 8, those indicators have asignificant value.

The current research findings confirm that ITUMAR canbe predicted by PU and PEOU, and PEOU is influenced byIDV and UA. The direct effects emphasize that PEOU andPU attained a strong positive direct influence on ITUMAR;therefore, hypotheses (𝐻2) and (𝐻3) are supported. PEOUis influenced positively by IDV; as a consequence, (𝐻4) issupported. Also, it is important to mention from Table 9the positive influence of IDV on PU and ITUMAR. The UAwith a path coefficient near to zero has no positive effects onPEOU.

The outer weights computed in Table 8 for UA haveno significance, and its index calculated for the students ishigh according to Table 11, which means that UA does notcontribute to PEOU of the MAR system, and as a conse-quence hypothesis (𝐻5) is rejected.

The variable PU has a negligible affectation path coef-ficient relationship of 0.063 on ITUMAR, and it is notsignificant for an error level of 5%; therefore (𝐻1) does nothold. This means that the influence of PU on ITUMAR isminimal or almost null.

Although ITUMAR2 indicator is not really an intentionto use this particular system, its benefits were designed forthe particular context of UAIM. The UAIM students areresponsible for its learning; therefore, the learning activitiesmust be performed even when the professor is absent. Theessential parts of theMAR application such as the interactionand interface can be reused to design other learning activities.

Themain variableswith their coefficient relationships andthe determination coefficients for accuracy prediction of themodel are shown in Figure 7. The IDV and UA have no 𝑅2values because they are exogenous variables, which meansthat they are not caused by another variable in the model.ITUMAR have a 𝑅2 value of 0.594, PEOU equals 0.219, andPU equals 0.550.Therefore, ITUMAR and PU are consideredmoderate and PEOU value is considered weak. The totaleffects between the variables are shown in Table 10. Therelations between PU→ITUMAR, UA→ITUMAR, UA→PU,and UA→PEOU are not statistically significant, which reaf-firm the level of significance coefficients obtained in Table 7and the outer weights of indicators obtained in Table 8.

As can be observed in Table 11, the IDV variable hasalways low values (<50) for all cases. Those values indicatethat, in this study, students tend to be more of collectivists.Regarding UA, it should be noted that all values are high.Those values indicate that, in this study, students have greatuncertainty of the unknown (new technology). This leadsto the conclusion that the system could be used easily if itoffers/promotes, in the future, a natural way for the collab-orative work. For example, at least two users are working atthe same time, on different devices, with the application.Thiswill exploit the ability of the student to work in a collaborativeway. In addition, the system could include help offered by

other participants to have more trust in the system use andas a consequence reduce the UA of the student.

5. Conclusions

In this study, a novel approach to the assessment of theeffects of the adoption of a MAR system for Mayo learningby undergraduates students was presented. The study wasbased on analyzing the interaction between the variablesrelated to perception of cultural dimensions and variablesrelated to the ITUMAR system.The application of the Loterıagame was based on AR, where students interact directly withthe educational content by relating images and audio. Theapplication plays a recording message with the name of theobject in the card to assist learning. The application wastested on mobile devices running Android operating system.According to the analysis of data collected from students afterusing the MAR system, the IDV as a cultural dimension hasgreat influence on the ITUMAR system.

In the future, the goal will be to extend the study byincreasing the number of undergraduates, creating subgroupsby gender, age, and cultural differences to relate it with theITU, and including the perceived enjoyment (PE) variable.It will also be important to work with the system redesignto support collaborative or group work. This will lead toharnessing the positive elements related to IDV index ofstudents. The consequence of promoting teamwork could bethe reduction of the uncertainty that is presented when thestudent uses a new technology. Finally, because the indicatorsrelated to UA used in this study were treated as a socialgroup type, it will be important to review these indicatorsfor each individual. This could improve the accuracy of themeasurements obtained.

Appendix

The questionnaire designed to obtain the information andvalidate the hypothesis is the following.

The Questionnaire. Please fill in the information about yourgeneral data (statistical purpose).

(SP1) Are you male or female?(SP2) Do you belong to an ethnic group?(SP3) How old are you?

Read the following statements and choose one of thefollowing options for each statement: 1 Strongly disagree, 2In disagreement 3 Somewhat disagree, 4 I do not know, 5Somewhat agree, 6 In agreement, 7 Strongly agree.

Perceived Usefulness

(PU1) I could improve my learning performance byusing the MAR system.

(PU2) I could enhance my language learning profi-ciency by using the MAR system.

(PU3) I could increase my learning productivity byusing the MAR system.

14 Mobile Information Systems

(PU4) I think that using the MAR system will helps melearn.

Perceived Ease of Use

(PEOU1) The design of the interface and the informationprovided by the MAR system is clear and easy tounderstand.

(PEOU2) It is easy to operate the MAR system.(PEOU3) Overall I feel it is easy forme to useMAR system.

Intention to Use the MAR

(ITUMAR1) I will intend to use theMAR system for activitiesconcerning the learning of Mayo language.

(ITUMAR2) The main principles of the system could be usedfor other learning activities.

Read the following statements and choose one of thefollowing options for each statement: 1 Strongly unimportant,2Without unimportant, 3 Somewhat unimportant, 4 I do notknow, 5 Somewhat important, 6 Important, 7 Very important.

Cultural Factors

Individualism

(CF1) Have sufficient time for your personal or homelife.

(CF2) Have security of studying.(CF3) The study is interesting.(CF4) My family and my friends respected my studies.

Read the following statements and choose one of thefollowing options for each statement: 1 Very poor, 2 Poor, 3Little poor, 4 Fair, 5 Little good, 6 Good, 7 Very good.

Uncertainty Avoidance

(CF5) Generally, how would you describe your state ofhealth these days?

Read the following statements and choose one of thefollowing options for each statement: 1 Usually, 2 Sometimes,3 Little, 4 I do not know, 5 It is rare, 6 Seldom, 7 Never.

(CF6) How often do you feel nervous or tense?

Read the following statements and choose one of thefollowing options for each statement: 1 Strongly disagree, 2In disagreement 3 Somewhat disagree, 4 I do not know, 5Somewhat agree, 6 In agreement, 7 Strongly agree.

(CF7) A person can be a good teacher even withoutknowing the answer to the question submittedto it relating to their work.

(CF8) The rules dictated by the school should neverbe broken, not even for the best interests of theschool.

Competing Interests

The authors declare that they have no competing interests.

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